Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory mid-point pricing. Buyers and sellers use Roth-Erev individual reinforcement learning to determine their price and quantity offers in each auction round. It is shown that market microstructure is strongly predictive for the relative market power of buyers and sellers, and that high market efficiency is generally attained. These findings are robust for tested changes in individual learning parameters. It is also shown that similar relative market power findings are obtained if the electricity buyer and seller populations instead each engage in social mimicry learning via a genetic algorithm. However, market efficiency is substantially reduced.